Rank-based estimation for autoregressive moving average time series models

Beth Andrews

doi:10.1111/j.1467-9892.2007.00545.x

Rank-based estimation for autoregressive moving average time series models

Beth Andrews^*

^*Corresponding author for this work

Statistics and Data Science

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449-1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.

Original language	English (US)
Pages (from-to)	51-73
Number of pages	23
Journal	Journal of Time Series Analysis
Volume	29
Issue number	1
DOIs	https://doi.org/10.1111/j.1467-9892.2007.00545.x
State	Published - Jan 1 2008

Keywords

Autoregressive moving average models
Rank estimation

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1111/j.1467-9892.2007.00545.x

Cite this

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AB - We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449-1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.

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Rank-based estimation for autoregressive moving average time series models

Abstract

Keywords

ASJC Scopus subject areas

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